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Adaptive Neural Control of Nonlinear Systems

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

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Abstract

The aim of the present paper is to integrate a recurrent neural network in two schemes of real-time soft computing neural control. There are applied the following control schemes: an indirect and a direct trajectory tracking control, using the state and parameter information, given by an identification recurrent neural network.

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References

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Author information

Authors and Affiliations

  1. CINVESTAV-IPN, Ave. IPN No. 2508 A. P. 14470, Mexico D. F., C.P.07360, Mexico

    Ieroham Baruch, Jose Martin Flores & Ruben Garrido

  2. IRI-UPC, Barcelona, Spain

    Federico Thomas

Authors
  1. Ieroham Baruch
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  2. Jose Martin Flores
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  3. Federico Thomas
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  4. Ruben Garrido
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Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

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© 2001 Springer-Verlag Berlin Heidelberg

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Baruch, I., Flores, J.M., Thomas, F., Garrido, R. (2001). Adaptive Neural Control of Nonlinear Systems. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_128

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  • DOI: https://doi.org/10.1007/3-540-44668-0_128

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

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